PROJECT SUMMARY/ABSTRACT The overarching goal of this proposal is to develop new and innovative analytic tools for longitudinal infant brain research and to leverage these tools to chart the development of infant brain networks during the first 6 months of life, a period of unparalleled postnatal growth and change. Describing the developmental trajectory of brain systems during this formative period has the potential to provide groundbreaking insights into major areas of scientific inquiry, including the identification of brain systems that underlie the development of cognitive functions, the discovery of how structural and functional network specializations arise, and the identification of brain networks that contribute to neuropsychiatric illness. However, despite this potential, longitudinal studies of infant brain development are still nascent, and prevailing analytic tools?largely designed for cross-sectional analyses of adult data?are ill-suited for fully capturing fast-pace developmental processes during infancy. This proposal aims to 1) develop innovative analytic tools that are specifically designed to address challenges inherent to longitudinal infant brain research; 2) leverage these tools to examine graph theoretic measures of brain network development in typical infancy; and 3) disseminate these tools and approaches to the broader research community. Methods development will focus on two key areas: registration (the approach for transforming individual brain images to a common space) and statistical analysis of longitudinal data (the approach for constructing and analyzing growth curves of brain development). Methods development and analyses will be conducted on anatomical, diffusion tensor imaging and resting- state functional MRI data collected from infants at three longitudinal time points between birth and 6 months of age. Aim 1 of this proposal is to develop and validate a novel hierarchical, tensor-based registration approach, designed to handle the challenges associated with registering highly heterogeneous images, a characteristic of longitudinal infant data. Aim 2 will improve an already state-of-the-art approach for the analysis of longitudinal data and pioneer its application to the case of longitudinal neuroimaging data. Finally, Aim 3 will leverage these methods to produce a temporally-precise mapping of typical growth curves of brain network development in the first postnatal months, providing a benchmark against which to interpret and understand how alternate trajectories of brain development can lead to disability. These aims will help advance the frontier of studies of brain development into early infancy, a formative, and yet relatively uncharted, period of development.